Cyber-physical additive manufacturing systems consists of tight integration of cyber and physical domains. This results in new cross-domain vulnerabilities that poses unique security challenges. One of the challenges is preventing confidentiality breach due to physical-to-cyber domain attacks, where attackers can use physical analog emissions to steal the cyber-domain information. This information theft is based on the idea that an attacker can accurately estimate the relation between the analog emissions (acoustics, power, electromagnetic emissions, etc.,) and the cyber-domain data (such as G-code). To obstruct this estimation process, it is crucial to generate computer aided manufacturing tools, such as slicing and tool-path generation algorithms, that are aware of these information leakage. In this thesis, we present a novel methodology that uses mutual information as a metric to quantize the information leakage from the side-channels, and demonstrates how various design variables (such as object orientation, nozzle velocity, etc.,) can be used in an optimization algorithm to minimize the information leakage. Our methodology integrates this leakage aware algorithms to the state-of-the-art slicing tools and achieves 24.76% average drop in the information leakage through acoustic side-channel. To the best of our knowledge, this is the first work that demonstrates the idea of generating information leakage aware computer aided manufacturing tools for protecting the confidentiality of the manufacturing system.

In the age of digitization, a layer of cyber software sits on a hardware circuit and controls the physical systems around us. The tight integration of cyber and physical components is referred to as Cyber-Physical Systems (CPS). The interactions between cyber and physical components brings unique challenges which traditional modeling tools struggle to resolve. Remarkably, they often fail to model the unintentional physical manifestation of cyber-domain information flows (side-channel signals), resulting in trust issues in the system.

This thesis takes a data-driven approach to model CPS behavior when exposed to various information flows. First, we demonstrate how to extract valuable cyber-domain information by recording the acoustic noise generated by a DNA synthesizer. Then, we consider an integrated circuit as a CPS by itself and monitor the chip through electromagnetic and power side-channels to detect hardware Trojans (HT) in the chip.

HT is a malicious modification of the hardware implementation of a circuit design which may lead to various security issues over the life-cycle of a chip. One of the major challenges for HT detection is its reliance on a trusted reference chip (a.k.a golden chip). However, in practice, manufacturing a golden chip is costly and often considered infeasible. This thesis investigates a creative neural network design and training methodology which eliminates the need for a golden chip. Furthermore, it proposes using hierarchical temporal memory (HTM) as a data-driven approach which can be updated over the chip's life-cycle and uses that for run-time HT detection.